As an important approach to overcome data silos and privacy concerns in deep learning, federated learning, which can jointly train the global model and keep data local, has shown remarkable performance in a range of industrial applications. However, federated learning still suffers from the problem that shared gradients may be subject to tampering, inference functions, and falsification. To address this issue, we propose a verifiable federated learning framework to deal with malicious aggregators. Initially, we propose a reputation calculation mechanism to solve the problem of selecting a reliable aggregator based on a multiweight subjective logic model. Furthermore, we design a verifiable federated learning scheme to ensure data confidentiality, integrity, and verifiability, as well as support the client's dynamic withdrawal. Security analyses indicate that our framework is secure against malicious adversaries. Furthermore, experimental results on real datasets show that our verifiable federated learning has high accuracy and feasible efficiency.

With the widespread use of end devices, online multi-label learning has become popular as the data generated by users using the Internet of Things devices have become huge and rapidly updated. However, in many scenarios, the user data are often generated in a geographically distributed manner that is often inefficient and difficult to centralize for training machine learning models. At the same time, current mainstream distributed learning algorithms always require a centralized server to aggregate data from distributed nodes, which inevitably causes risks to the privacy of users. To overcome this issue, we propose a distributed approach for multi-label classification, which trains the models in distributed computing nodes without sharing the source data from each node. In our proposed method, each node trains its model with its local online data while it also learns from the neighbour nodes without transferring the training data. As a result, our proposed method achieved the online distributed approach for multi-label classification without losing performance when taking existing centralized algorithms as a reference. Experiments show that our algorithm outperforms the centralized online multi-label classification algorithm in F1 score, being 0.0776 higher in macro F1 score and 0.1471 higher for micro F1 score on average. However, for the Hamming loss, both algorithms beat each other on some datasets, and our proposed algorithm loses 0.005 compared to the centralized approach on average, which can be neglected. Furthermore, the size of the network and the degree of connectivity are not factors that affect the performance of this distributed online multi-label learning algorithm.

The primary goal of online change detection (OCD) is to promptly identify changes in the data stream. OCD problem find a wide variety of applications in diverse areas, e.g., security detection in smart grids and intrusion detection in communication networks. Prior research usually assumes precise knowledge of the system parameters. Nevertheless, this presumption often proves unattainable in practical scenarios due to factors such as estimation errors, system updates, etc. This paper aims to take the first attempt to develop a triadic-OCD framework with certifiable robustness, provable optimality, and guaranteed convergence. In addition, the proposed triadic-OCD algorithm can be realized in a fully asynchronous distributed manner, easing the necessity of transmitting the data to a single server. This asynchronous mechanism could also mitigate the straggler issue that faced by traditional synchronous algorithm. Moreover, the non-asymptotic convergence property of Triadic-OCD is theoretically analyzed, and its iteration complexity to achieve an ǫ-optimal point is derived. Extensive experiments have been conducted to elucidate the effectiveness of the proposed method.

A kind new computation generalized integral method based on evolution strategy algorithm is proposed, this method selects the break point willfully according to the integrand variable interval, as evolution strategy initial group, optimized these break points through the evolution strategy algorithm, which may obtain some most superior break point finally, then sumed again, again according to the sum function definition fitness function, in assigned under termination condition,which may attain precision high integral value. Finally, taked the generalized integral (infinite integral), the double integral (flaw integral) as the example, the simulation result indicated that this algorithm compares traditional some methods, which has the computation precision to be high, auto-adapted strong characteristics and so on.

Identity-based proxy re-encryption (IB-PRE) converts the ciphertext encrypted under the delegator’s identity to the one encrypted under the delegatee’s identity through a semitrusted proxy without leaking delegator’s private key and the underlying plaintext. At present, the security of most IB-PRE schemes relies on the hardness of the discrete logarithm solution or large integer decomposition and cannot resist attacks of the quantum algorithms. The majority of the IB-PRE schemes over lattice are secure only in the random oracle model. Aiming at such problems, the paper constructs a secure IB-PRE scheme over lattice in the standard model. In the scheme, the underlying encryption scheme proposed by Gentry et al. in EUROCRYPT 2010 is adopted to reduce the storage space of ciphertext. The proposed scheme is unidirectional collusion-resistant multihop and anonymous, and it is semantically secure against selective identity and chosen plaintext attack based on Decisional Learning With Errors with uniformly distributed errors (D-U-LWE) hard problem in the standard model.